van Hateren, Hans (2019): Dataset from an active aeolian system in the Dutch coastal dunes: sediment grain size and shape data obtained between 2016 and 2019 using dynamic image analysis [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.909106, Supplement to: van Hateren, Johannes Albert; van Buuren, Unze; Arens, Sebastiaan Martinus; van Balen, Ronald Theodorus; Prins, Maarten Arnoud (in review): Identifying sediment transport mechanisms from grain size-shape distributions. Earth Surface Dynamics Discussion, https://doi.org/10.5194/esurf-2019-58
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Abstract:
This dataset includes 1) A sediment grain size-shape dataset from an active aeolian system in the Dutch coastal dunes and 2) artificial grain size-shape datasets. Both were used to prove applicability of a novel method for the detection of sediment transport processes from a sedimentary deposit. The new method makes use of end-member modelling on grain size-shape distributions. The size and shape data were obtained using dynamic image analysis (video's) of sediment samples. End-member modelling was performed using AnalySize (Paterson and Heslop, 2015. DOI: https://doi.org/10.1002/2015GC006070 )
The “Dune dataset” consists of sediment samples from the surface and from sediment traps. The samples were obtained from a coastal dune area south of the town Ijmuiden in the Netherlands (for coordinates of- and info on the samples, refer to Coordinates_and_info_dunedataset.xlsx).
The samples were measured using dynamic image analysis. For each sediment grain in each sample, various shape variables were measured (aspect ratio = AR, convexity = con, Cox circularity = Cc). Size was also measured as the area equivalent size (D2d).
Based on the class-limits described in grainsize_grainshape_classlimits.xlsx, each sediment grain was assigned to the right class. By summing the volume in each size class, and normalising to a sum of 100%, grain size distributions were obtained (D2d).
Furthermore, by assigning each grain to the right size-shape class (see size_shape_distribution_classes.xlsx), grain size-shape distributions were obtained (also summing to 100% volume). ConD2d for example, is a distribution of grain convexity and grain size.
The artificial datasets consist of similar data as the "Dune dataset", but the data were generated by creating random mixtures of several grain size-shape distributions using a random number generator.
Further details:
Paterson, Greig A; Heslop, David (2015): New methods for unmixing sediment grain size data. Geochemistry, Geophysics, Geosystems, 16(12), 4494-4506, https://doi.org/10.1002/2015GC006070
Coverage:
Median Latitude: 52.425000 * Median Longitude: 4.570000 * South-bound Latitude: 52.420000 * West-bound Longitude: 4.560000 * North-bound Latitude: 52.430000 * East-bound Longitude: 4.580000
Event(s):
Parameter(s):
| # | Name | Short Name | Unit | Principal Investigator | Method/Device | Comment |
|---|---|---|---|---|---|---|
| 1 | Event label | Event | van Hateren, Hans | |||
| 2 | File content | Content | van Hateren, Hans | |||
| 3 | File name | File name | van Hateren, Hans | |||
| 4 | File format | File format | van Hateren, Hans | |||
| 5 | File size | File size | kByte | van Hateren, Hans | ||
| 6 | Uniform resource locator/link to file | URL file | van Hateren, Hans |
License:
Creative Commons Attribution 4.0 International (CC-BY-4.0)
Size:
35 data points
Data
| 1 Event | 2 Content | 3 File name | 4 File format | 5 File size [kByte] | 6 URL file |
|---|---|---|---|---|---|
| Bloemendaal_Dunes | Coordinates of- and info on the samples | Coordinates-and-info_dunedataset | XLSX | 167.378 | Coordinates-and-info_dunedataset.xlsx |
| Class-limits | Grainsize_Grainshape_classlimits | XLSX | 11.380 | Grainsize_Grainshape_classlimits.xlsx | |
| Size-shape classes | Size-shape_distributions_classes | XLSX | 161.488 | Size-shape_distributions_classes.xlsx | |
| Bloemendaal_Dunes | Grain size distributions | Dunedataset_distributions | XLSX | 8819.793 | Dunedataset_distributions.xlsx |
| Artificial dataset used to test the method of end-member modelling on grain size-shape distributions | 3EM_nonoise_artificialdataset | XLSX | 9894.957 | 3EM_nonoise_artificialdataset.xlsx | |
| Artificial dataset used to test the method of end-member modelling on grain size-shape distributions | 4EM_noise_artificialdataset | XLSX | 10203.944 | 4EM_noise_artificialdataset.xlsx | |
| Artificial dataset used to test the method of end-member modelling on grain size-shape distributions | 4EM_noise_highmix_artificialdataset | XLSX | 9768.434 | 4EM_noise_highmix_artificialdataset.xlsx |